何が起きたか: LFM2.5-Encoders for Fast Long-Context Inference on CPU

Liquid AI released two encoder models, LFM2.5-Encoder-230M and 350M, optimized for fast CPU inference on documents up to 8,192 tokens.

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LFM2.5-Encoders for Fast Long-Context Inference on CPU

Hugging Face · 公開日 2026年7月28日 · 読了3分

30秒で要点

何が起きたか: Liquid AI released two encoder models, LFM2.5-Encoder-230M and 350M, optimized for fast CPU inference on documents up to 8,192 tokens.
なぜ重要か: Engineers building classification, routing, or extraction systems that run continuously on existing hardware and need to process long documents cheaply.
注意点: Benchmarks use PyTorch eager mode on unspecified CPU hardware. No ONNX or quantized exports yet, limiting production deployment options for high-volume serving.

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LFM2.5-Encoders for Fast Long-Context Inference on CPU

Team Article Published July 28, 2026

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Fernando Fernandes Neto fernandofernandes Follow

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Leonie Monigatti iamleonie Follow

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Today, we release two new encoder models on Hugging Face: LFM2.5-Encoder-230M and LFM2.5-Encoder-350M . They match the quality of larger models but stay fast as inputs get longer. This means you can run document-scale jobs on the hardware you already have, even on CPU.

Here's what you get:

Strong for their size: match or beat larger encoders on GLUE, SuperGLUE, and multilingual tasks.

8,192-token context with latency that grows slowly as inputs get longer.

Fast on CPU : about 3.7× faster than ModernBERT-base at long context.

With these, you can build intent routers, policy linters, PII detectors, and text classifiers that run cheaply, all day. See the live demos below.

Why we built a general-purpose encoder

Last month we released LFM2.5-Retrievers , built for multilingual search. LFM2.5-Encoders come from the same family but serve a broader purpose. They're pre-trained with a masked-language objective, so you can fine-tune them for classification, token-level tasks, and search alike. Search is just one thing an encoder enables. That's why we bu

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LFM2.5-Encoders for Fast Long-Context Inference on CPU

Hugging Face · 公開日 2026年7月28日 · 読了3分

30秒で要点

何が起きたか: Liquid AI released two encoder models, LFM2.5-Encoder-230M and 350M, optimized for fast CPU inference on documents up to 8,192 tokens.
なぜ重要か: Engineers building classification, routing, or extraction systems that run continuously on existing hardware and need to process long documents cheaply.
注意点: Benchmarks use PyTorch eager mode on unspecified CPU hardware. No ONNX or quantized exports yet, limiting production deployment options for high-volume serving.

この要約を音声で聴く

記事より

Back to Articles

a]:hidden">

LFM2.5-Encoders for Fast Long-Context Inference on CPU

Team Article Published July 28, 2026

Upvote 45

+39

Fernando Fernandes Neto fernandofernandes Follow

LiquidAI

Edoardo Mosca EdoardoMosca Follow

LiquidAI

Maxime Labonne mlabonne Follow

LiquidAI

Leonie Monigatti iamleonie Follow

LiquidAI

Here's what you get:

Strong for their size: match or beat larger encoders on GLUE, SuperGLUE, and multilingual tasks.

8,192-token context with latency that grows slowly as inputs get longer.

Fast on CPU : about 3.7× faster than ModernBERT-base at long context.

With these, you can build intent routers, policy linters, PII detectors, and text classifiers that run cheaply, all day. See the live demos below.

Why we built a general-purpose encoder

原文からの抜粋です。全文は配信元でお読みください。

inference

The Agent Architect

1つのパターン、1つのトレードオフ、1つの本番障害事例。エージェントシステムを構築する人のための短い週刊ブリーフィング。

週1回のメール、ワンクリックで購読解除できます。アドレスはブリーフィングの送信のみに使用します。